Online research in philosophy

Over the past decade, sequence learning has gradually become a central paradigm through which to study implicit learning. In this chapter, we start by briefly summarizing the results obtained with different variants of the sequence learning paradigm. We distinguish three subparadigms in terms of whether the stimulus material is generated either by following a fixed and repeating sequence (e.g., Nissen & Bullemer, 1987), by relying on a complex set of rules from which one can produce several alternative deterministic sequences (e.g., Lewicki, Hill & Bizot, 1988; Stadler, 1989), or by following the output of a probabilistic set of rules such as instantiated by noisy finite-state grammars (Cleeremans & McClelland, 1991; Jiménez, Mendéz & Cleeremans, 1996). Next, we focus on the processes involved in sequence representation and acquisition. We suggest that the sensitivity to the sequential structure observed in the probabilistic subparadigm can only be a result of the acquisition of a representation of the statistical constraints of the material, and that this sensitivity emerges through the operation of mechanisms that are well instantiated by connectionist models such as the Simple Recurrent Network (Elman, 1990; Cleeremans, 1993b). We present new simulation work meant to explore to what extent the model can also account for specific data obtained in a paradigmatic instance of deterministic, rule-based sequence learning task: Lewicki et al. (1988)'s situation. Finally, we report on the results of an experiment that compares learning on otherwise similar deterministic and probabilistic structures, and we show that learning of both types of structures is equivalent only under conditions that maximally hinder explicit acquisition. Taken together, these simulation and experimental data lend support to the claim that implicit learning in all three sequence learning subparadigms can amount to a form of statistical sequence learning. They also suggest that distinguishing among several theories of sequence representation and acquisition may require us to analize the data in great detail. Hopefully, however, some truth can be found in such details..

Implicit learning is the process through which we become sensitive to certain regularities in the environment (1) in the absence of intention to learn about those regularities (2) in the absence of awareness that one is learning, and (3) in such a way that the resulting knowledge is difficult to express.

Can associative learning take place without awareness? We explore this issue in a sequence learning paradigm with amnesic and control participants, who were simply asked to react to one of four possible stimuli on each trial. Unknown to them, successive stimuli occurred in a sequence. We manipulated the extent to which stimuli followed the sequence in a deterministic manner (noiseless condition) or only probabilistically so (noisy condition). Through this paradigm, we aimed at addressing two central issues: first, we asked whether sequence learning takes place in either condition with amnesic patients. Second, we asked whether this learning takes place without awareness. To answer this second question, participants were asked to perform a subsequent sequence generation task under inclusion and exclusion conditions, as well as a recognition task. Reaction times results show that amnesic patients learned the sequence only in the deterministic condition. However, they failed to be able to reproduce the sequence in the generation task. In contrast, we found learning for both sequence structures in control participants, but only control participants exposed to a deterministic sequence were successful in performing the generation task, thus suggesting that the acquired knowledge can be used consciously in this condition. Neither amnesic nor control participants showed correct old/new judgments in the recognition task. The results strengthen the claim that implicit learning is at least partly spared in amnesia, and the role of contextual information available for learning is discussed. © 2006 Elsevier Ltd. All rights reserved.

Implicit learning – broadly construed as learning without awareness – is a complex, multifaceted phenomenon that defies easy definition. Frensch (1998) listed as many as eleven definitions in an overview, a diversity that is undoubtedly symptomatic of the conceptual and methodological challenges that continue to pervade the field forty years after the term first appeared in the literature (Reber, 1967). According to Berry and Dienes (1993), learning is implicit when an individual acquires new information without intending to do so and in such a way that the resulting knowledge is difficult to express. In this, implicit learning thus contrasts strongly with explicit learning (e.g., as when learning how to solve a problem or learning a concept), which is typically hypothesisdriven and fully conscious. Implicit learning is the process through which one becomes sensitive to certain regularities in the environment: (1) without trying to learn regularities, (2) without knowing that one is learning regularities, and (3) in such a way that the resulting knowledge is unconscious.

Running head: Implicit sequence learning ABSTRACT Can we learn without awareness? Although this issue has been extensively explored through studies of implicit learning, there is currently no agreement about the extent to which knowledge can be acquired and projected onto performance in an unconscious way. The controversy, like that surrounding implicit memory, seems to be at least in part attributable to unquestioned acceptance of the unrealistic assumption that tasks are process-pure, that is, that a given task exclusively involves either implicit or explicit knowledge.

What do people learn when they do not know that they are learning? Until recently, all of the work in the area of implicit learning focused on empirical questions and methods. In this book, Axel Cleeremans explores unintentional learning from an information-processing perspective. He introduces a theoretical framework that unifies existing data and models on implicit learning, along with a detailed computational model of human performance in sequence-learning situations.